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https://issues.apache.org/jira/browse/TIKA-2298?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15964635#comment-15964635
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ASF GitHub Bot commented on TIKA-2298:
--------------------------------------
asmehra95 commented on issue #159: Creation of TIKA-2298 contributed by
asmehra95- Import of vgg16 via Deeplearning4j
URL: https://github.com/apache/tika/pull/159#issuecomment-293141458
hello folks,
I have fixed formatting issues @thammegowda please review it. Let me know if
any changes are required.
I have made it a little more customizable. You can now choose if you want to
save model to disk or not.
Saving a model to disk requires a lot of memory( around 500mb ) but it saves
a lot of runtime memory once the model is saved.
How to use:
add a field in config file
```xml
<param name="serialize" type="string">no</param>
```
It can be yes or no
Observations:
When loading model from disk:
It only require around 1200mb of ram to run.
When model is loaded from h5 files using helper functions
It requires 2500mb of ram to run the model.
I think we can distribute serialized models for vgg16 instead of the
original hash files. Will it produce any problems @saudet @agibsonccc , One
more thing, the VGG16 model doesn't work completely offline. It connects to
internet after processing the image to decode output. Can we make it entirely
offline?
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> To improve object recognition parser so that it may work without external
> RESTful service setup
> -----------------------------------------------------------------------------------------------
>
> Key: TIKA-2298
> URL: https://issues.apache.org/jira/browse/TIKA-2298
> Project: Tika
> Issue Type: Improvement
> Components: parser
> Affects Versions: 1.14
> Reporter: Avtar Singh
> Labels: ObjectRecognitionParser
> Fix For: 1.15
>
> Original Estimate: 672h
> Remaining Estimate: 672h
>
> When ObjectRecognitionParser was built to do image recognition, there wasn't
> good support for Java frameworks. All the popular neural networks were in
> C++ or python. Since there was nothing that runs within JVM, we tried
> several ways to glue them to Tika (like CLI, JNI, gRPC, REST).
> However, this game is changing slowly now. Deeplearning4j, the most famous
> neural network library for JVM, now supports importing models that are
> pre-trained in python/C++ based kits [5].
> *Improvement:*
> It will be nice to have an implementation of ObjectRecogniser that
> doesn't require any external setup(like installation of native libraries or
> starting REST services). Reasons: easy to distribute and also to cut the IO
> time.
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